Evaluating the impacts of soil data on hydrological and nonpoint source pollution prediction

Integrating material flow analysis into hydrological model for water environment management of large-scale urban-rural mixed catchment

Simultaneous simulation of urban and rural hydrological processes is important for water environment management of mixed land-uses catchments. However, the discharge paths of pollution in the urban drainage system are not described in traditional catchment hydrological models. In this study, an urban-rural water environment (URWE) model is developed through incorporating the material flow analysis (MFA) and the soil and water assessment tool (SWAT) into a general framework. The URWE model is an advancement with respect to traditional hydrological models in terms of simultaneously simulating the urban organized and rural decentralized discharges of pollution. Due to the low data requirement and high computational efficiency of MFA, URWE model is applicable to large-scale catchment with wide urban area. The URWE model is applied to a typical urban-rural mixed catchment, the Dianchi Catchment (China), where the pollution characteristics are analyzed and the pollution control measures are investigated. Results indicate that the URWE model outperforms the conventional SWAT model for both water quantity and quality simulations, with an 8.5 % improvement in average coefficient of determination (R2) and a 67.4 % improvement in average Nash coefficient (NSE). Rural best management practice, rainwater-sewage separation, and storage capacity expansion are identified as the most cost-effective measures for COD, TN, and TP reduction, respectively. Contributions of this study are to improve the accuracy of water environment simulation in urban-rural mixed catchment, as well as to help decision-makers develop synergistic urban-rural water environment management measures.

Hybrid deep learning based prediction for water quality of plain watershed

Establishing a highly reliable and accurate water quality prediction model is critical for effective water environment management. However, enhancing the performance of these predictive models continues to pose challenges, especially in the plain watershed with complex hydraulic conditions. This study aims to evaluate the efficacy of three traditional machine learning models versus three deep learning models in predicting the water quality of plain river networks and to develop a novel hybrid deep learning model to further improve prediction accuracy. The performance of the proposed model was assessed under various input feature sets and data temporal frequencies. The findings indicated that deep learning models outperformed traditional machine learning models in handling complex time series data. Long Short-Term Memory (LSTM) models improved the R2 by approximately 29% and lowered the Root Mean Square Error (RMSE) by about 48.6% on average. The hybrid Bayes-LSTM-GRU (Gated Recurrent Unit) model significantly enhanced prediction accuracy, reducing the average RMSE by 18.1% compared to the single LSTM model. Models trained on feature-selected datasets exhibited superior performance compared to those trained on original datasets. Higher temporal frequencies of input data generally provide more useful information. However, in datasets with numerous abrupt changes, increasing the temporal interval proves beneficial. Overall, the proposed hybrid deep learning model demonstrates an efficient and cost-effective method for improving water quality prediction performance, showing significant potential for application in managing water quality in plain watershed.

From field soil sampling to watershed model: Upscaling by integrating information entropy and interpolation method

Soil property data plays a crucial role in watershed hydrology and non-point source (H/NPS) modeling, but how to improve modeling accuracy with affordable soil samplings and the effects of sampling information on H/NPS modeling remains to be further explored. In this study, the number of sampling points and soil properties were optimized by the information entropy and the spatial interpolation method. Then the sampled properties were parameterized and the effects of different parameterization schemes on H/NPS modeling were tested using the Soil and Water Assessment Tool (SWAT). The results indicated that the required sampling points increased successively for soil bulk density (SOL_BD), soil saturated hydraulic conductivity (SOL_K) and soil available water capacity (SOL_AWC). Compared to the traditional database (Harmonized world soil database), the NSE and R2 performance by new scheme increased by 22.8% and 10.5%, respectively. The entropy-based optimization reduced the sampling points by 13.2%, indicating a more cost-effective scheme. Compared to hydrological simulation, sampled properties showed greater effects on NPS modeling, especially for nitrogen. This proposed method/framework can be generalized to other watersheds by upscaling field soil sampling information to the watershed scale, thus improving H/NPS simulation.

Nonpoint Source Pollution (NPSP) Induces Structural and Functional Variation in the Fungal Community of Sediments in the Jialing River, China

Nonpoint source pollution (NPSP) from human production and life activities causes severe destruction in river basin environments. In this study, three types of sediment samples (A, NPSP tributary samples; B, non-NPSP mainstream samples; C, NPSP mainstream samples) were collected at the estuary of the NPSP tributaries of the Jialing River. High-throughput sequencing of the fungal-specific internal transcribed spacer (ITS) gene region was used to identify fungal taxa. The impact of NPSP on the aquatic environment of the Jialing River was revealed by analysing the community structure, community diversity, and functions of sediment fungi. The results showed that the dominant phylum of sediment fungi was Rozellomycota, followed by Ascomycota and Basidiomycota (relative abundance > 5%). NPSP caused a significant increase in the relative abundances of Exosporium, Phialosimplex, Candida, Inocybe, Tausonia, and Slooffia, and caused a significant decrease in the relative abundances of Cercospora, Cladosporium, Dokmaia, Setophaeosphaeria, Paraphoma, Neosetophoma, Periconia, Plectosphaerella, Claviceps, Botrytis, and Papiliotrema. These fungal communities therefore have a certain indicator role. In addition, NPSP caused significant changes in the physicochemical properties of Jialing River sediments, such as pH and available nitrogen (AN), which significantly increased the species richness of fungi and caused significant changes in the fungal community β-diversity (P < 0.05). pH, total phosphorus (TP), and AN were the main environmental factors affecting fungal communities in sediments of Jialing River. The functions of sediment fungi mainly involved three types of nutrient metabolism (symbiotrophic, pathotrophic, and saprotrophic) and 75 metabolic circulation pathways. NPSP significantly improved the pentose phosphate pathway, pentose phosphate pathway, and fatty acid beta-oxidation V metabolic circulation pathway functions (P < 0.05) and inhibited the chitin degradation to ethanol, super pathway of heme biosynthesis from glycine, and adenine and adenosine salvage III metabolic circulation pathway functions (P < 0.05). Hence, NPSP causes changes in the community structure and functions of sediment fungi in Jialing River and has adversely affected for the stability of the Jialing River Basin ecosystem.

Pollution simulation and remediation strategy of a zinc smelting site based on multi-source information

Contaminated sites pose a significant risk to human health and the regional environment. A comprehensive study was dedicated to improving the understanding of the contamination condition of a smelting site by integrating multi-source information through 3D visualization techniques. The results showed that 3D visualization reveals excellent potential for application in the environmental studies to finely depict contamination in soils and establish relationships with geological features, hydrological conditions, and sources of contamination. The contamination plume model revealed that the soil environment at the site was seriously threatened by toxic metals, and dominated by multi-metal contamination, with contamination soil volume ranked as Cd > As > Pb> Zn > Hg. The stratigraphic model revealed the heterogeneous geological conditions of the site and identified the mixed fill layer as the primary remediation soil layer. The permeability model revealed that soil permeability significantly influenced contamination dispersion and contributed to delineate the contamination boundary accurately. The ecological hazard model targeted the high ecological hazard area and determined the high hazard contribution of Cd and Hg in the site soil. The outcomes can be directly applied to actual site remediation and provide a reference for the contaminated sites evaluation and restoration in the future.

Quantifying the Contribution of Agricultural and Urban Non-Point Source Pollutant Loads in Watershed with Urban Agglomeration

Urban agglomeration is a new characteristic of the Chinese urbanization process, and most of the urban agglomeration is located in the same watershed. Thus, urban non-point source (NPS) pollution, especially the characteristic pollutants in urban areas, aggravates NPS pollution at the watershed scale. Many agricultural studies have been performed at the watershed scale; however, few studies have provided a study framework for estimating the urban NPS pollution in an urban catchment. In this study, an integrated approach for estimating agricultural and urban NPS pollution in an urban agglomeration watershed was proposed by coupling the Soil and Water Assessment Tool (SWAT), the event mean concentration (EMC) method and the Storm Water Management Model (SWMM). The Hun-Taizi River watershed, which contains a typical urban agglomeration and is located in northeastern China, was chosen as the study case. The results indicated that the per unit areas of total nitrogen (TN) and total phosphorus (TP) in the built-up area simulated by the EMC method were 11.9% and 23 times higher than the values simulated by the SWAT. The SWAT greatly underestimated the nutrient yield in the built-up area. This integrated method could provide guidance for water environment management plans considering agricultural and urban NPS pollution in an urban catchment.

Multi-model ensemble simulated non-point source pollution based on Bayesian model averaging method and model uncertainty analysis

Watershed models are cost-effective and powerful tools for evaluating and controlling non-point source pollution (NPSP), while the reliability of watershed models in a management context depends largely on inherent uncertainties in model predictions. The objective of this study is to present the use of multi-model ensemble applied to streamflow, total nitrogen (TN), and total phosphorus (TP) simulation and quantify the uncertainty resulting from model structure. In this study, three watershed models, which have different structures in simulating NPSP, were selected to conduct watershed monthly streamflow, TN load, and TP load ensemble simulation and 90% credible intervals based on Bayesian model averaging (BMA) method. The result using the observed data of the Yixunhe watershed revealed that the coefficient of determination and Nash-Sutcliffe coefficient of the BMA model simulate streamflow, TN load, and TP load were better than that of the single model. The higher the efficiency of a single model is, the greater the weight during the BMA ensemble simulation is. The 90% credible interval of BMA has a high coverage of measured values in this study. This indicates that the BMA method can not only provide simulation with better precision through ensemble simulation but also provide quantitative evaluation of the model structure through interval, which could offer rich information of the NPSP simulation and management.

Emergy-Based Evaluation of Changes in Agrochemical Residues on the Qinghai–Tibet Plateau, China

Study of changes in agrochemical residues on the Qinghai–Tibet Plateau is necessary for the agricultural green development of the fragile plateau and its downstream regions. The total agrochemical residue (TR) caused by main agrochemical inputs was estimated in the study area of Qinghai province and the Tibet Autonomous Region over 1995–2017 by using the emergy synthesis method. The total agrochemical residue was decomposed into the intensity factor, the structure factor, the productivity factor, and the labour factor by using the Logarithmic Mean Divisia Index (LMDI) decomposition method. The change in TR could be divided into four time periods, i.e., a rapidly increasing period during 1995–1998, a stable period during 1999–2004, a slowly increasing period during 2005–2011, and a fluctuant period during 2012–2017. The study area had a mean TR intensity in area (TRA) of 3.31 × 1014 sej/ha, which was only 38.21% of that in China; however, the annual growth rate of TRA in the study area was 2.93%, higher than the rate of 1.91% in China over 1995–2017. The study area had a mean TR intensity in production (TRP) of 4.06 × 1010 sej/CNY (Chinese Yuan), which was 71.05% of that in China; however, the annual decreasing rate of TRP in the study area was 0.95%, lower than the rate of 1.98% in China over 1995–2017. All the LMDI decomposed factors contributed to the TR increase during 1995–1998; the intensity factor, the structure factor, and the labour factor contributed to the TR decrease during 1999–2004; the structure factor and the productivity factor contributed to the TR increase during 2005–2011; and only the productivity factor contributed to the TR increase during 2012–2017. Compared with the whole country, the study area has more potential to reduce TR by improving agrochemical use efficiency, strengthening the recovery of plastic film residue, increasing organic agricultural materials, raising the efficiency of agricultural production, and accelerating the transfer of rural labours to secondary and tertiary industries.

Water quality characterization of storm and irrigation runoff from a container nursery

Commercial nurseries grow specialty crops for resale using a variety of methods, including containerized production, utilizing soilless substrates, on a semipervious production surface. These "container" nurseries require daily water application and continuous availability of mineral nutrients. These factors can generate significant nutrients [total nitrogen (TN), and total phosphorus (TP)] and sediment [total suspended solids (TSS)] in runoff, potentially contributing to eutrophication of downstream water bodies. Runoff is collected in large ponds known as tailwater recovery basins for treatment and reuse or discharge to receiving streams. We characterized TSS, TN, and TP, electrical conductivity (EC), and pH in runoff from a 5.2 ha production portion of a 200-ha commercial container nursery during storm and irrigation events. Results showed a direct correlation between TN and TP, runoff and TSS, TN and EC, and between flow and pH. The Storm Water Management Model (SWMM) was used to characterize runoff quantity and quality of the site. We found during irrigation events that simulated event mean concentrations (EMCs) of TSS, TN, and TP were 30, 3.1 and 0.35 mg·L^-1, respectively. During storm events, TSS, TN and TP EMCs were 880, 3.7, and 0.46 mg·L^-1, respectively. EMCs of TN and TP were similar to that of urban runoff; however, the TSS EMC from nursery runoff was 2-4 times greater. The average loading of TSS, TN and TP during storm events was approximately 900, 35 and 50 times higher than those of irrigation events, respectively. Based on a 10-year SWMM simulation (2008-2018) of runoff from the same nursery, annual TSS, TN and TP load per ha during storm events ranged from 9230 to 13,300, 65.8 to 94.0 and 9.00 to 12.9 kg·ha^-1·yr^-1, respectively. SWMM was able to characterize runoff quality and quantity reasonably well. Thus, it is suitable for characterizing runoff loadings from container nurseries.

Mapping the scientific research on non-point source pollution: a bibliometric analysis

A bibliometric analysis was conducted to examine the progress and future research trends of non-point source (NPS) pollution during the years 1991-2015 based on the Science Citation Index Expanded (SCI-Expanded) of Web of Science (WoS). The publications referencing NPS pollution were analyzed including the following aspects: document type, publication language, publication output and characteristics, subject category, source journal, distribution of country and institution, author keywords, etc. The results indicate that the study of NPS pollution demonstrated a sharply increasing trend since 1991. Article and English were the most commonly used document type and language. Environmental sciences and ecology, water resources, and engineering were the top three subject categories. Water science and technology ranked first in distribution of journal, followed by Science of the total environment and Environmental Monitoring and Assessment. The USA took a leading position in both quantity and quality, playing an important role in the research field of NPS pollution, followed by the UK and China. The most productive institution was the Chinese Academy of Sciences (Chinese Acad Sci), followed by Beijing Normal University and US Department of Agriculture's Agricultural Research Service (USDA ARS). The analysis of author keywords indicates that the major hotspots of NPS pollution from 1991 to 2015 contained "water," "model," "agriculture," "nitrogen," "phosphorus," etc. The results provide a comprehensive understanding of NPS pollution research and help readers to establish the future research directions.